Global companies are producing far more content than just a few years ago, and many are struggling to keep up. A new enterprise report by Smartcat shows that content demands are rising sharply while teams attempt to expand into new markets, adopt AI tools, and maintain compliance across a growing number of channels.
The result is a growing operational strain: organizations need faster production, localized messaging, and strict governance, yet many still rely on fragmented workflows and uneven AI adoption.
Content Demands Are Surging Across Global Companies
Nearly all enterprise teams say their content workloads increased in the past year. In fact, 98% of organizations report higher content demand year over year, and 73% say workloads have grown beyond stable levels.
This growth is being driven by more than marketing. Modern content now spans customer communications, internal training, product documentation, and partner resources. As companies expand internationally and operate across more digital channels, the amount of material required to support employees and customers has multiplied.
Many organizations are trying to manage this surge without major increases in budget or headcount, turning content operations into a bottleneck that slows down campaigns, product launches, and global updates.
Global Expansion Is Driving Complexity
Language expansion is one visible part of the challenge. More than half of enterprise teams added at least one new language in the past year, reflecting continued expansion into new regions.
But the real complexity goes beyond translation. Global teams must also adapt messaging to local markets, comply with regional regulations, and update information across multiple platforms at once.
Content that once appeared in a single campaign may now need to be customized for different regions, industries, and channels while remaining consistent with brand and legal requirements. Keeping that material accurate across markets has become one of the biggest operational challenges for global companies.
AI Is Increasing Output — But Mostly at the Task Level
To manage rising workloads, enterprises are increasingly adopting artificial intelligence. Most organizations report early gains from AI in content creation and research.
About 80% say AI helps speed up drafting and content production, while 68% report faster research and summarization tasks.
However, these improvements are often limited to individual steps rather than entire workflows. Roughly 64% of organizations use AI to automate specific parts of the content lifecycle, but only 32% report fully orchestrated workflows where tasks, approvals, and handoffs are integrated.
As a result, many global teams still manage approvals, revisions, and publishing manually across multiple tools.
Governance Reviews Are Slowing AI Deployment
Even when companies adopt AI tools, deployment can be slow.
About 38% of enterprises report recurring delays caused by security, legal, or compliance reviews before AI systems can be implemented or expanded.
At large organizations, AI initiatives often require approval from multiple departments responsible for risk management, privacy, and regulatory compliance. These review processes can set the pace of AI adoption regardless of how capable the technology itself has become.
The issue highlights a growing challenge for enterprise AI: success increasingly depends on governance frameworks and operational readiness, not just the underlying models.
AI Skills Remain Uneven Across the Workforce
Another barrier is training. Despite widespread experimentation with AI tools, many employees still lack structured guidance on how to use them effectively.
According to the report, 58% of enterprises lack formal AI training programs. About 34% rely on self-directed learning, while 24% offer no organized training at all.
This uneven skill distribution often leaves AI usage concentrated among a small group of employees who experiment with the technology on their own, rather than being embedded across teams.
Organizations with structured AI training are significantly more likely to report higher returns from AI investments, particularly in areas such as localization, market adaptation, and workflow automation.
Workflow Integration Separates High-Performing AI Teams
The companies reporting the strongest results from AI share a common pattern: they embed AI directly into operational workflows rather than using it as a standalone tool.
Teams with the highest AI returns are 6.5 times more likely to achieve significantly faster localization and global content updates, allowing them to roll out changes across markets simultaneously instead of region by region.
They are also more likely to use unified technology stacks, reducing the number of tools and handoffs required to move content from drafting to publication.
This level of integration allows organizations to scale content production without expanding headcount — a critical advantage as global operations continue to grow.
The Next Challenge for Enterprise Content
The surge in enterprise content production indicates a change in how global organizations operate. Companies now communicate with employees, customers, and partners across more markets, platforms, and regulatory environments than ever before.
AI is beginning to ease the workload, but most companies are still in the early stages of integrating it into their core operations.
For many organizations, the next phase will be building coordinated workflows, governance frameworks, and workforce skills that allow AI-driven content systems to operate reliably at global scale.
